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Accurate segmentation of tumors and cysts in dental panoramic radiographs is crucial for effective diagnosis and treatment. However, conventional CNN-based approaches face challenges due to indistinct boundaries, structural complexity, and noise from varying imaging conditions. To address these limitations, we propose Dentaldiff, a novel diffusion-based segmentation model. Our method introduces a dynamic feature fusion strategy and an iterative denoising mechanism to enhance global feature extraction and noise robustness. Additionally, modified DenseUNet was designed to improve segmentation performance. The model achieves state-of-the-art performance, with mean IoU of 0.61 ± 0.07 and Dice score of 0.75 ± 0.05, outperforming existing methods. Dentaldiff effectively handles complex anatomical structures and is the first known application of diffusion models to dental panoramic segmentation. Experimental results show that Dentaldiff achieves higher segmentation performance compared to CNN-based methods, particularly in challenging cases with indistinct boundaries and noise, suggesting its potential for broader clinical application.